Add json parsing example in java,python and csv example in scala

build.sbt

@@ -14,7 +14,7 @@ libraryDependencies ++= Seq(
   "org.apache.commons" % "commons-lang3" % "3.0",
   "org.eclipse.jetty"  % "jetty-client" % "8.1.14.v20131031",
   "com.typesafe.play" % "play-json_2.10" % "2.2.1",
-  "com.eclipsesource.minimal-json" % "minimal-json" % "0.9.1",
+  "com.fasterxml.jackson.core" % "jackson-databind" % "2.3.3",
   "org.elasticsearch" % "elasticsearch-hadoop-mr" % "2.0.0.RC1",
   "net.sf.opencsv" % "opencsv" % "2.0"
 )

pom.xml

@@ -28,9 +28,9 @@
       <version>8.1.14.v20131031</version>
     </dependency>
     <dependency>
-      <groupId>com.eclipsesource.minimal-json</groupId>
-      <artifactId>minimal-json</artifactId>
-      <version>0.9.1</version>
+      <groupId>com.fasterxml.jackson.core</groupId>
+      <artifactId>jackson-databind</artifactId>
+      <version>2.3.3</version>
     </dependency>
     <dependency>
       <groupId>org.apache.commons</groupId>

src/main/java/com/oreilly/learningsparkexamples/java/BasicLoadJson.java

@@ -0,0 +1,52 @@
+/**
+ * Illustrates loading a json file and finding out if people like pandas
+ */
+package com.oreilly.learningsparkexamples.java;
+
+import java.io.StringReader;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Iterator;
+import java.lang.Iterable;
+import scala.Tuple2;
+
+import org.apache.commons.lang.StringUtils;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.FlatMapFunction;
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+
+public class BasicLoadJson {
+
+  public static class Person {
+    public String name;
+    public Boolean lovesPandas;
+  }
+
+  public static class ParseJson implements FlatMapFunction<Iterator<String>, Person> {
+    public Iterable<Person> call(Iterator<String> lines) throws Exception {
+      ArrayList<Person> people = new ArrayList<Person>();
+      ObjectMapper mapper = new ObjectMapper();
+      while (lines.hasNext()) {
+        String line = lines.next();
+        people.add(mapper.readValue(line, Person.class));
+      }
+      return people;
+    }
+  }
+
+  public static void main(String[] args) throws Exception {
+		if (args.length != 2) {
+      throw new Exception("Usage BasicLoadJson [sparkMaster] [jsoninput]");
+		}
+    String master = args[0];
+    String fileName = args[1];
+		JavaSparkContext sc = new JavaSparkContext(
+      master, "basicloadjson", System.getenv("SPARK_HOME"), System.getenv("JARS"));
+    JavaRDD<String> input = sc.textFile(fileName);
+    JavaRDD<Person> result = input.mapPartitions(new ParseJson());
+    List<Person> resultCollection = result.collect();
+	}
+}

src/main/scala/com/oreilly/learningsparkexamples/scala/BasicParseCsv.scala

@@ -0,0 +1,31 @@
+/**
+ * Illustrates a simple map partition to parse CSV data in Scala
+ */
+package com.oreilly.learningsparkexamples.scala
+
+import java.io.StringReader
+
+import org.apache.spark._
+import play.api.libs.json._
+import play.api.libs.functional.syntax._
+import scala.util.parsing.json.JSON
+
+import au.com.bytecode.opencsv.CSVReader
+
+object BasicParseCsv {
+  def main(args: Array[String]) {
+    if (args.length < 2) {
+      println("Usage: [sparkmaster] [inputfile]")
+      exit(1)
+    }
+    val master = args(0)
+    val inputFile = args(1)
+    val sc = new SparkContext(master, "BasicParseCsv", System.getenv("SPARK_HOME"))
+    val input = sc.textFile(inputFile)
+    val result = input.map{ line =>
+      val reader = new CSVReader(new StringReader(line));
+      reader.readNext();
+    }
+    println(result.collect().mkString(","))
+    }
+}

src/python/LoadJson.py

@@ -0,0 +1,13 @@
+from pyspark import SparkContext
+import json
+import sys
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print "Error usage: LoadJson [sparkmaster] [file]"
+        sys.exit(-1)
+    sc = SparkContext(sys.argv[1], "LoadJson")
+    input = sc.textFile(sys.argv[2])
+    data = input.map(lambda x: json.loads(x))
+    print data.collect()
+